methodology

Data Vault Modeling

Data Vault Modeling is a data modeling methodology designed for building scalable, flexible, and auditable data warehouses in enterprise environments. It structures data into three core components: hubs (business keys), links (relationships), and satellites (descriptive attributes), enabling incremental loading and historical tracking. This approach supports agile development and integrates data from multiple sources while maintaining data lineage and compliance.

Also known as: Data Vault, DV, Data Vault 2.0, Data Vault Methodology, DV Modeling
🧊Why learn Data Vault Modeling?

Developers should learn Data Vault Modeling when working on large-scale data warehousing projects that require handling complex, evolving business requirements and integrating disparate data sources. It is particularly useful in industries like finance, healthcare, or logistics where auditability, scalability, and real-time data integration are critical, as it reduces rework and supports regulatory compliance through built-in historization.

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